Bibliometrix (https://www.bibliometrix.org/) allows R users to import a bibliography database generated using SCOPUS and Web of Science stored either as a Bibtex (.bib) or Plain Text (.txt) file.
The package has simple functions which allows for descriptive analyses as shown in table-1 to table-3.
The analysis can also be easily visualised as shown in figure-1.
library(bibliometrix) #load the package
library(pander) #other required packages
library(knitr)
library(kableExtra)
library(ggplot2)
library(bibliometrixData)
# use scopuscollection data from the package Manuscripts
# including the term 'bibliometrics' in the title. Period:
# 1975 - 2017 Database: SCOPUS Format: bibtex
data("scopusCollection")
file1 = data("scopusCollection")
# M=convert2df(file='insert
# filename',format='bibtex',dbsource = 'scopus')#convert
# the data to data frame
# scopusCollection=convert2df(file='scopus.bib',dbsource =
# 'scopus',format='bibtex')
# Descriptive analysis
M = scopusCollection #just to reuse the other code
res1 = biblioAnalysis(M, sep = ";")
s1 = summary(res1, k = 10, pause = FALSE, verbose = FALSE)
d1 = s1$MainInformationDF #main information
d2 = s1$MostProdAuthors #Most productive Authors
d3 = s1$MostCitedPapers #most cited papers
pander(d1, caption = "Summary Information")
| Description | Results |
|---|---|
| MAIN INFORMATION ABOUT DATA | |
| Timespan | 1975:2017 |
| Sources (Journals, Books, etc) | 280 |
| Documents | 487 |
| Average years from publication | 13.6 |
| Average citations per documents | 10.36 |
| Average citations per year per doc | 0.6601 |
| References | 12245 |
| DOCUMENT TYPES | |
| article | 417 |
| book | 12 |
| conference | 58 |
| DOCUMENT CONTENTS | |
| Keywords Plus (ID) | 1436 |
| Author’s Keywords (DE) | 722 |
| AUTHORS | |
| Authors | 949 |
| Author Appearances | 1187 |
| Authors of single-authored documents | 162 |
| Authors of multi-authored documents | 787 |
| AUTHORS COLLABORATION | |
| Single-authored documents | 184 |
| Documents per Author | 0.513 |
| Authors per Document | 1.95 |
| Co-Authors per Documents | 2.44 |
| Collaboration Index | 2.6 |
pander(d3, caption = "Most Cited Papers")
| Paper | DOI | TC | TCperYear | NTC |
|---|---|---|---|---|
| DAIM TU , 2006, TECHNOL FORECAST SOC CHANGE | 331 | 19.47 | 6.07 | |
| BORGMAN CL , 2002, ANNU REV INF SCI TECHNOL | 312 | 14.86 | 4.49 | |
| WEINGART P, 2005, SCIENTOMETRICS | 208 | 11.56 | 8.25 | |
| NARIN F, 1994, SCIENTOMETRICS | 169 | 5.83 | 1.98 | |
| CRONIN B, 2001, J INF SCI | 160 | 7.27 | 2.89 | |
| HOOD WW , 2001, SCIENTOMETRICS | 144 | 6.55 | 2.60 | |
| HICKS D , 2015, NATURE | 130 | 16.25 | 30.20 | |
| CHEN Y-C , 2011, SCIENTOMETRICS | 129 | 10.75 | 7.17 | |
| D’ANGELO CA , 2011, J AM SOC INF SCI TECHNOL | 81 | 6.75 | 4.50 | |
| GLNZEL W , 2006, SCIENTOMETRICS | 78 | 4.59 | 1.43 |
p1 = plot(res1, pause = FALSE)
p1[[3]]
## Summary Plot-4 (Average Article Citation)
p1[[4]]
* A graph for author statistics over time can also be produced.
topAU = authorProdOverTime(M, k = 10, graph = TRUE)
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries",
sep = ";")
# Plot the network
net = networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration",
type = "circle", size = TRUE, remove.multiple = FALSE, labelsize = 0.7,
cluster = "none")
Country Collaboration
threeFieldsPlot(M, fields = c("DE", "AU", "CR"))
library(gridExtra)
CS = conceptualStructure(M, field = "DE", method = "CA", minDegree = 4,
clust = 5, stemming = FALSE, labelsize = 10, documents = 10,
graph = FALSE)
grid.arrange(CS[[4]], CS[[5]], CS[[6]], CS[[7]], ncol = 2, nrow = 2)
Conceptual Structure
Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.
# Map2=thematicEvolution(M3,field='ID',n=1000,stemming=FALSE,repel=TRUE,years=2000)
Map = thematicMap(M, field = "ID", n = 1000, minfreq = 5, stemming = FALSE,
size = 0.5, n.labels = 4, repel = TRUE)
plot(Map$map)
biblioshiny()
This concludes the example. There are various online sources to take this further